AI Edge Systems
Intelligence at the AI Edge Systems
On-device and near-edge AI for privacy, offline resilience, and decisions that can’t wait on the cloud.
Run AI where the data already is
We design edge AI systems that run on devices, gateways, or private local servers — for clinics, factories, field apps, and products that need low latency, data residency, or offline capability.
On-device inference
Models optimized for phones, tablets, kiosks, and embedded hardware.
Near-edge gateways
Local servers that process sensitive data before anything leaves the site.
Privacy & residency
Keep PHI, footage, and documents local when cloud is not an option.
Hybrid sync
Edge-first workflows with optional cloud sync when connectivity returns.
On-device inference
Models optimized for phones, tablets, kiosks, and embedded hardware.
Privacy & residency
Keep PHI, footage, and documents local when cloud is not an option.
Near-edge gateways
Local servers that process sensitive data before anything leaves the site.
Hybrid sync
Edge-first workflows with optional cloud sync when connectivity returns.
On-device inference
Models optimized for phones, tablets, kiosks, and embedded hardware.
Near-edge gateways
Local servers that process sensitive data before anything leaves the site.
Privacy & residency
Keep PHI, footage, and documents local when cloud is not an option.
Hybrid sync
Edge-first workflows with optional cloud sync when connectivity returns.
Why edge AI?
Not every workflow can round-trip to the cloud. Edge systems cut latency, protect sensitive data, and keep products usable offline — without giving up modern AI capabilities.
Top challenges we solve
Cloud-only designs fail offline
We plan for intermittent connectivity and local fallbacks.
Device resource limits
Model choice and quantization match the hardware you actually ship.
Unclear privacy boundaries
We map what stays on-device vs. what may sync — before build.
AI that stays close to the user
Edge-first products feel faster and safer — especially in healthcare, field ops, and regulated environments.
See related work
Selected case studies connected to this capability area.
Frequently asked questions
Phone, gateway, or on-prem server?+
Depends on the use case — we recommend the lightest layer that meets latency and privacy needs.
Can we still use cloud models sometimes?+
Yes — hybrid designs are common: edge for sensitive/fast paths, cloud for heavier jobs.
Do you optimize models for edge?+
Yes — quantization, distillation, and runtime choice are part of delivery when needed.
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